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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Author Correction: CRISPR screens reveal convergent targeting strategies against evolutionarily distinct chemoresistance in cancer

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Nature communications 2025年第1期16卷 1569页

作者： Chunge Zhong Wen-Jie Jiang Yingjia Yao Zexu Li You Li Shengnan Wang Xiaofeng Wang Wenjuan Zhu Siqi Wu Jing Wang Shuangshuang Fan Shixin Ma Yeshu Liu Han Zhang Wenchang Zhao Lu Zhao Yi Feng Zihan Li Ruifang Guo Li Yu Fengyun Pei Jun Hu Xingzhi Feng Zihuan Yang Zhengjia Yang Xueying Yang Yue Hou Danni Zhang Dake Xu Ren Sheng Yihao Li Lijun Liu Hua-Jun Wu Jun Huang Teng Fei Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health Sciences Northeastern University Shenyang 110819 China. National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110819 China. Foshan Graduate School of Innovation Northeastern University Foshan 528311 China. Peking University Third Hospital Beijing 100191 China. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Clinical Research Center the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. Guangdong Institute of Gastroenterology Guangzhou China. Department of Cardiothoracic Surgery Jinqiu Hospital of Liaoning Province Shenyang China. Department of Thoracic Surgery The Fourth Affiliated Hospital of China Medical University Shenyang China. Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education) School of Materials Science and Engineering Northeastern University Shenyang China. BeiGene Institute BeiGene (Shanghai) Research & Development Co. Ltd 200131 Shanghai China. Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Peking University Cancer Hospital & Institute Beijing 100142 China. hjwu@***. Department of Biomedical Informatics School of Basic Medical Sciences Peking University Health Science Center Beijing 100191 China. hjwu@***. Center for Precision Medicine Multi-Omics Research Institute of Advanced Clinical Medicine Peking University Beijing 100191 China. hjwu@***. Department of Colorectal Surgery the Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China. huan

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Structured regression on multilayer networks 16

Structured regression on multilayer networks

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16th SIAM International Conference on data Mining 2016, SDM 2016

作者： Polychronopoulou, Athanasia Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia United States

ISBN: (纸本)9781510828117

Until recently, research in social network analysis was focused on single layer networks, where only one type of links among nodes is considered. This approach does not consider the variety of interactions that exist among nodes, resulting in the loss of a large amount of information. In the last few years there is an advanced interest in multilayer networks analysis, where multiple types of nodal connections are considered jointly. In most approaches however the contributions of the various interactions are averaged, resulting again in the loss of information. In this work we present a structured regression model for node attribute prediction in multilayer networks. Our Gaussian Conditional Random Fields model is designed to maximize the information gained from the use of data with multiple layers of graphical structure. Our model accommodates graphs with layers that share the same set of nodes allowing for missing nodes and unobserved connections. At the same time it models the evolution of such networks over time without requiring the addition of a new layer. We present evidence that this model outperforms the traditionally used one and that it offers predictive accuracy that increases as the number of layers used grows, on both synthetic data and challenging real world applications such as predicting citation count and sepsis hospitalization admission rate at all hospitals in California. Copyright © by SIAM.

关键词： Hospitals

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Predicting poll trends using twitter and multivariate time-series classification 8th

Predicting poll trends using twitter and multivariate time-s...

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8th International Conference on Social informatics, SocInfo 2016

作者： Mirowski, Tom Roychoudhury, Shoumik Zhou, Fang Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia United States

ISBN: (纸本)9783319478791

Social media outlets, such as Twitter, provide invaluable information for understanding the social and political climate surrounding particular issues. Millions of people who vary in age, social class, and political beliefs come together in conversation. However, this information poses challenges to making inferences from these tweets. Using the tweets from the 2016 U.S. Presidential campaign, one main research question is addressed in this work. That is, can accurate predictions be made detecting changes in a political candidate’s poll score trends utilizing tweets created during their campaign? The novelty of this work is that we formulate the problem as a multivariate time-series classification problem, which fits the temporal nature of tweets, rather than as a traditional attribute-based classification. Features that represent various aspects of support for (or against) a candidate are tracked on an hour-by-hour basis. Together these form multivariate time-series. One commonly used approach to this problem is based on the majority voting scheme. This method assumes the univariate time-series from different features have equal importance. To alleviate this issue a weighted shapelet transformation model is proposed. Extensive experiments on over 12 million tweets between November 2015 and January 2016 related to the four primary candidates (Bernie Sanders, Hillary Clinton, Donald Trump and Ted Cruz) indicate that the multivariate time-series approach outperforms traditional attribute-based approaches. © Springer International Publishing AG 2016.

关键词： Surveys

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Fusing web and audio predictors to localize the origin of music pieces for geospatial retrieval 38th

Fusing web and audio predictors to localize the origin of mu...

引用

38th European Conference on Information Retrieval Research, ECIR 2016

作者： Schedl, Markus Zhou, Fang Department of Computational Perception Johannes Kepler University Linz Austria Center for Data Analytics and Biomedical Informatics Temple University Philadelphia United States

ISBN: (纸本)9783319306704

Localizing the origin of a music piece around the world enables some interesting possibilities for geospatial music retrieval, for instance, location-aware music retrieval or recommendation for travelers or exploring non-Western music – a task neglected for a long time in music information retrieval (MIR). While previous approaches for the task of determining the origin of music either focused solely on exploiting the audio content or web resources, we propose a method that fuses features from both sources in a way that outperforms standalone approaches. To this end, we propose the use of block-level features inferred from the audio signal to model music content. We show that these features outperform timbral and chromatic features previously used for the task. On the other hand, we investigate a variety of strategies to construct web-based predictors from web pages related to music pieces. We assess different parameters for this kind of predictors (e.g., number of web pages considered) and define a confidence threshold for prediction. Fusing the proposed audio-and web-based methods by a weighted Borda rank aggregation technique, we show on a previously used dataset of music from 33 countries around the world that the median placing error can be reduced from 1,815 to 0 kilometers using K-nearest neighbor regression. © Springer International Publishing Switzerland 2016.

关键词： Music

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Distance based modeling of interactions in structured regression 25

Distance based modeling of interactions in structured regres...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Stojkovic, Ivan Jelisavcic, Vladisav Milutinovic, Veljko Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University PhiladelphiaPA United States Mathematical Institute Serbian Academy of Sciences and Arts Belgrade Serbia School of Electrical Engineering University of Belgrade Belgrade Serbia

Graphical models, as applied to multi-target prediction problems, commonly utilize interaction terms to impose structure among the output variables. Often, such structure is based on the assumption that related outputs need to be similar and interaction terms that force them to be closer are adopted. Here we relax that assumption and propose a feature that is based on distance and can adapt to ensure that variables have smaller or larger difference in values. We utilized a Gaussian Conditional Random Field model, where we have extended its originally proposed interaction potential to include a distance term. The extended model is compared to the baseline in various structured regression setups. An increase in predictive accuracy was observed on both synthetic examples and real-world applications, including challenging tasks from climate and healthcare domains.

关键词： Artificial intelligence

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